Demonstration and Evaluation of State-of-the-Art Wastewater Collection Systems Condition Assessment Technologies
Bibliographic record
Abstract
Condition assessment of wastewater collection systems is a vital part of a utility’s asset management program. Reliable information on pipe condition is needed to prioritize rehabilitation and replacement projects, given the current state of our nation’s infrastructure. Although inspections with conventional closed-circuit television (CCTV) have been the mainstay of pipeline condition assessment for decades, other technologies are now commercially available. Five of these innovative technologies were selected for field trials under the U.S. Environmental Protection Agency (USEPA) demonstration program: zoom camera, electroscanning, digital scanning, laser profiling, and sonar. The goal of the field demonstration was to evaluate the technical performance and cost of these technologies. The field demonstration was conducted in August 2010 and was hosted by the Kansas City, Missouri Water Services Department. The innovative technologies were compared to CCTV inspection. Each technology identified maintenance and structural defects by collecting data or images of the pipe condition. The camera technologies (i.e., digital scanning, zoom camera, and CCTV) and laser scanning provided pipe condition above the water line, whereas sonar assessed conditions below the water line. Electroscanning detected leakage-related defects anywhere along the pipe circumference. Costs were compared for different inspection technologies based on actual costs for planning, field work, data analysis, and reporting. Total costs for the multisensor (digital, laser, and sonar scanning) inspection were $14.71 per m of pipeline inspected as compared to $10.31 per m for electroscanning, $3.46 per m for zoom camera, and $9.78 to $10.48 per m for CCTV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".